Written corrective feedback plays a vital role in second language acquisition. It serves not only to enhance learners’ language proficiency but also to foster their understanding of grammar rules, structures, and the appropriate use of vocabulary. CiteSpace is a robust tool for literature analysis that facilitates researchers in gaining comprehensive insights into the structure and evolutionary trends of academic fields by visualizing literature networks and analyzing citation patterns. The Web of Science core collection database was used as the primary source for data collection. There were 184 effective articles were selected from 2019 to 2023. In recent years, there has been significant progress in research on written corrective feedback, particularly in the development of automated error correction systems and personalized feedback tools that use machine learning and natural language processing techniques. Researchers are also emphasizing the importance of customized correction strategies tailored to individual learner differences. Additionally, the use of big data and corpora to analyze learner writing data provides empirical support for second language writing teaching. In the foreseeable future, the progression of artificial intelligence technology alongside the facilitation of interdisciplinary research is anticipated to deepen these trends, thereby fostering the ongoing innovation and refinement of teaching methodologies and theories pertaining to second language writing.

Authors:
Weihao Shi, Xi'an Yanta No.2 Middle School, China

About the Presenter(s)
Mr. Shi obtained his master degree in applied linguistics from Xi'an International Studies University. His research mainly focus on teacher education & development and writing assessment. Now, he as an secondary English teacher at Xi'an, China.

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